Systematic review of interventions for mental health, cognition and psychological well-being in long COVID
Bibliographic record
Abstract
AIMS: This systematic review aims to identify and synthesise the publicly available research testing treatments for mental health, cognition and psychological well-being in long COVID. METHODS: The following databases and repositories were searched in October-November 2023: Medline, Embase, APA PsycINFO, Cumulative Index to Nursing and Allied Health Literature, China National Knowledge Internet, WANFANG Data, Web of Science's Preprint Citation Index, The Cochrane Central Register of Controlled Trials, Clinicaltrials.gov and the WHO International Clinical Trials Registry Platform. Articles were selected if they described participants with long COVID symptoms at least 4 weeks after SAR-CoV-19 infection, reported primary outcomes on mental health, cognition and/or psychological well-being, and were available with at least an English-language summary. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for systematic reviews were followed. RESULTS: Thirty-three documents representing 31 studies were included. Seven tested psychosocial interventions, five pharmaceutical interventions, three natural supplement interventions, nine neurocognitive interventions, two physical rehabilitation interventions and five integrated interventions. While some promising findings emerged from randomised controlled trials, many studies were uncontrolled; a high risk of bias and insufficient reporting were also frequent. CONCLUSIONS: The published literature on treatments for mental health, cognition and psychological well-being in long COVID show that the interventions are highly heterogeneous and findings are inconclusive to date. Continued scientific effort is required to improve the evidence base. Regular literature syntheses will be required to update and educate clinicians, scientists, interventionists and the long COVID community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".